Arctic chironomid training set from Canada, Greenland, Iceland and Svalbard for estimating paleotemperatures (2022)
Bibliographic record
Abstract
This dataset includes chironomid species assemblage data and air temperature estimates from 400+ lakes across northern North America, Greenland, Iceland, and Svalbard to inform interpretations of Holocene subfossil chironomid assemblages used in paleolimnological reconstruction. This calibration-set was developed by re-identifying and taxonomically harmonizing chironomids in previously described surface sediment samples, with identifications made at finer taxonomic resolution than in original publications (which are cited in the publication describing this dataset, Medeiros et al. 2022 Quaternary Science Reviews, and should be cited by dataset users). Site summer air temperatures are newly estimated with a consistent method using the WorldClim 2.1 gridded bioclimatic dataset. The large geographic coverage of this dataset is intended to provide climatic analogs for a wide range of Holocene climates in the northwest North Atlantic region and North American Arctic, including Greenland. For many of these regions, modern calibration data for paleoclimate proxies are sparse despite keen interest in paleoclimate reconstructions from high latitudes. Dataset users should consult and cite the following source publication: Medeiros, A.S., Chipman, M., Francis, D.R., Hamerlik, L., Langdon, P., Puleo, P.J.K., Schellinger, G., Steigleder, R., Walker, I.R., Woodroffe, S., and Axford, Y. 2022. A continent-scale chironomid training set for reconstructing arctic temperatures. Quaternary Science Reviews 294, 107728. DOI 10.1016/j.quascirev.2022.107728.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".